Beyond the Mass Balance: Watershed phosphorus legacies and the evolution of the current water quality policy challenge
Bibliographic record
Abstract
Increased use of phosphorus (P) fertilizers and detergents, as well as the growth of animal feeding operations, have more than doubled P inputs to human-impacted watersheds over pre-industrial levels. While P fertilizer use and manure application help to maximize crop yields, excess P is lost to runoff, leading to eutrophication of downstream waters—a phenomenon of great concern in the North American Great Lakes region. Excess P also accumulates across the landscape, leading to legacies that serve as long-term sources of P to surface waters, even after inputs to the watershed are reduced. We developed, for the first time, a process-based model, ELEMeNT-P, designed to capture legacy P accumulation and depletion trajectories along the land-aquatic continuum. To drive the model, we reconstructed a more than 100-year trajectory of P inputs to the Grand River Watershed (GRW), Canada’s largest river basin draining directly to Lake Erie. Our results show that since 1900 the GRW has served as a net P sink, with an estimated accumulation of more than 480 ktons P, of which 89% resides in soils and 6% in reservoirs and riparian areas. Future simulations suggest that while a 40% reduction in P discharge to Lake Erie is possible under aggressive management scenarios, legacy P will continue to elevate P loads to Lake Erie for centuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".